GENOTYPE x ENVIRONMENT INTERACTION AND YIELD STABILITY OF STRIGA RESISTANT SORGHUM [Sorghum bicolor (L.) MOENCH] HYBRIDS IN ETHIOPIA

Abstract

Sorghum known as a Camel crop of cereals, is among the dominant staple food grains for the majority of Ethiopians. In spite of biotic and abiotic stress tolerance, the procedures in the selection of good performing and stable genotypes are complicated by the phenomenon of genotype by environment interaction; therefore, interaction is the major concern to plant breeders to develop improved varieties/hybrids. Forty nine sorghum genotypes (hybrids and open pollinated varieties) were evaluated at five environments during the 2016 main cropping season. The objectives of this study were to estimate the magnitude and nature of GEI for yield and yield related traits and to determine yield stability of striga resistant sorghum genotypes in the dry lowland areas of Ethiopia. The study was conducted using a simple lattice design with two replications at each environment. The result of the combined analysis of variance for grain yield revealed very highly significant (P≤0.001) difference among environment (E), genotype (G) and genotype × environment interaction (GEI). Environment explained 76.13% of the total (G + E +GE) variation, whereas G and GE explained 11.21% and 12.66% of the total variation, respectively. The magnitude of the environment used was 6.8 times greater than the genotype, implying that most of the variation in grain yield was due to the environment. Based on the combined analysis of variance over locations, the mean grain yield of environments ranged from 588 kg ha-1 in Humera to 4508 kg ha-1 in Sheraro. The highest yield was obtained from ESH-1 (3278 kg ha-1), while the lowest was from K5136 (735 kg ha-1) and the average grain yield of genotypes was 2184 kg ha-1. Different stability models: AMMI Stability Value (ASV), Yield Stability Index (YSI), Regression coefficient (bi) and Deviation from Regression (S2di) were used to identify stable genotypes. Yield was significantly correlated with bi (0.91), r2 (0.55) and ASV (-0.56) while it was not correlated with S2di (-0.26). Generally, AMMI model and GGE biplot were better for partitioning the GEI into the causes of variation and the best multivariate models in this study. Thus, AMMI model was used to identify superior genotypes for specific and wide adaptation. Accordingly, K7439, K7252 and K7437 were specifically adapted to low environments of Humera, Kobo and Fedis, whereas, ESH-1 and K7233 were were the better hybrids for favorable environments of Mehoni and Sheraro, respectively. Moreover, the GGE biplot identified two different sorghum growing mega-environments for grain yield. The first mega environment includes higher (Mehoni) to low yielding (Humera, Kobo and Fedis) environments, respectively, with the winner genotype ESH-1 and the second mega environment containing the highest yielding environment in Sheraro area with winner genotype K7233. Thus, the which-won-where biplot showed two winning genotypes in two mega environments. However, the standard hybrid check, ESH-1 won in most of the environments. In order to give more reliable recommendation this experiment should be repeated at least for one year. Key words: AMMI,